Radar systems are well known in a variety of applications. Expensive, complex radars are known which have sophisticated mechanisms for detecting a target. However a large number of applications do not justify the expense of a complex and high cost radar system. For instance, low cost radars are often used in maritime applications, such as on small boats etc. In such applications with relatively simple radar systems the detection of small targets in sea and land clutter is often quite difficult. Fast moving targets can also be difficult to detect accurately.
There are known ways to improve target detection by processing of radar data however the amount of processing that can be done on the signals from a low cost radar system is somewhat limited.
An Extended Kalman Filter uses a linear approximation of system dynamics and updates an estimate of target position using information from previous data and a new measurement. However although the Extended Kalman Filter works on non-linear systems it assumes Gaussian noise. Noise is not always Gaussian, depending on the receiver type. Often clutter is the limiting problem, not noise—and clutter is often non-Gaussian.
A Particle Filter represents the probability of a target being in a particular position by a cloud of weighted particles. Each of these particles changes position in one time step in a way determined by an estimate of target dynamics. Over time several particles' weights tend to zero. In a re-sampling step particles with the lowest weight are removed and new ones are created near to the heaviest weighted particles. The Particle Filter solves the problem of non-Gaussian noise, but it can be slow to run on large data sets. A real time system is required.
The Viterbi algorithm creates a scoring function based on the log-likelihood ratio for a set of hypothetical paths and chooses the most likely path. Coherent Track Before Detect schemes use Doppler information to estimate target velocity. This can then be used in other algorithms to aid tracking. A Coherent Track Before Detect scheme may run in real time but requires a coherent radar, which can be expensive relative to a non-coherent one.
A 3D Matched Filter takes the 3-dimensional Fourier Transform of the time-2D image space and uses this to estimate the motion energy for a set of velocity vectors. The vector with the maximum energy is chosen as the target track.
Adaptive Constant False Alarm Rate (CFAR) threshold setting works by passing a statistics window over the scene and estimating parameters of a distribution. A clutter rejection threshold is then set based on that distribution and a probability of false alarm (PFA). However statistics used in CFAR threshold setting are often assumed to be Rayleigh or Gaussian when this is not necessarily the case.
GB patent application 1, 605, 307 describes a target detection system for radar which stores signals received over time from the same direction. The stored signals are then integrated in various combinations selected to include elements indicative of a target moving in a particular way. If a target is present and moving in the same way as one of the selected combinations the integrated signal exceeds a threshold. Thus improved target detection can be achieved. However the method would struggle to identify two targets moving with different velocities.